An Automatic Method for Counting Olive Trees in Very High Spatial Remote Sensing Images

In this paper, we propose an automatic method for counting olive trees in very high spatial remote sensing images. In a first step, olive trees are separated from other land-cover classes present in the image by means of a Gaussian process classifier (GPC). Due to the important role of the spatial information in very high resolution imagery, we feed the GPC with different morphological features computed from the original image. The output of this step is a binary classification map containing olive trees seen as foreground and other classes as background. In the second step, the number of blobs in the image representing possible olive trees is counted using an automatic procedure. Each blob is considered valid if its size is within a range specified a priori referring to the real size of trees. Experimental results obtained on a very high spatial remote sensing image acquired by the IKONOS-2 sensor are reported.